Skip to main content
SpringerLink
Log in

Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Cloud computing affords lot of resources and computing facilities through Internet. Cloud systems attract many users with its desirable features. In spite of them, Cloud systems may experience severe security issues. Thus, it is essential to create an Intrusion Detection System (IDS) to detect both insider and outsider attacks with high detection accuracy in cloud environment. This work proposes an anomaly detection system at the hypervisor layer named Hypervisor Detector that uses a hybrid algorithm which is a mixture of Fuzzy C-Means clustering algorithm and Artificial Neural Network (FCM-ANN) to improve the accuracy of the detection system. The proposed system is implemented and compared with Naïve Bayes classifier and Classic ANN algorithm. The DARPA’s KDD cup dataset 1999 is used for experiments. Based on extensive theoretical and performance analysis, it is evident that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate even for low frequent attacks thereby outperforming Naïve Bayes classifier and Classic ANN.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Hai J, Guofu X, Deqing Z (2013) A VMM-based intrusion prevention system in cloud computing environment. J Supercomput Springer Sci + Bus Media 66(3):1133–1151

    Google Scholar 

  2. Oktay U, Sahingoz, OK (2013) Attack types and intrusion detection systems in cloud computing. In: Proceedings of 6th International Information Security & Cryptology Conference, p 71–76, 2013, Ankara, Turkey

  3. Vieira K, Schulter A, Westphall C, Westphall C (2010) Intrusion detection techniques in grid and cloud computing environment. IEEE IT Prof Mag 2010:38–43

    Article  Google Scholar 

  4. SanjayRam M, Velmurugan N, Thirukumaran S (2012) Effective analysis of cloud based intrusion detection system. Int J Comput Appl Inform Technol 1(2):16–22

    Google Scholar 

  5. Amjad HB, Sabyasachi P, Debasish J (2013) Machine learning approach for intrusion detection on cloud virtual machines. Int J Appl Innov Eng Manag 2(6):57–66

    Google Scholar 

  6. Dunlap GW, King ST, Cinar S, Basrai M, Chen PM (2002) Revirt: enabling intrusion analysis through virtual machine logging and replay. In: Proceedings of 5th symposium on operating systems design and implementation. USENIX, Boston, pp 211–224

    Chapter  Google Scholar 

  7. Chirag NM, Dhiren RP, Avi P, Muttukrishnan R (2012) Integrating Signature Apriori based Network Intrusion Detection System (NIDS) in Cloud Computing. In: Proceedings of 2nd International Conference on Communication, Computing & Security, Procedia Technology, 6:905–912. doi:10.1016/j.protcy.2012.10.110

  8. Feng Z, Hai J (2012) Automated approach to intrusion detection in VM-based dynamic execution Environment. Comput Inform 31:271–297

    Google Scholar 

  9. Garfinkel T, Rosenblum M (2003) A virtual machine introspection based architecture for intrusion detection. In: Proceedings of Network and Distributed Systems Security Symposium (NDSS), p 191–206, 2003

  10. Kourai K, Chiba S (2005) HyperSpector: virtual distributed monitoring environments for secure intrusion detection. In: Proceedings of 1st ACM/USENIX international conference on virtual execution environments. ACM, Chicago, pp 197–207

    Chapter  Google Scholar 

  11. Bakshi A, Yogesh B (2010) Securing cloud from DDOS attacks using intrusion detection system in virtual machine. In: Proceedings of second International Conference on Communication Software and Networks, p 260–264. doi:10.1109/ICCSN.2010.56

  12. Mazzariello C, Bifulco R, Canonoco R (2010) Integrating a network IDS into an Open source Cloud computing environment. In: Information Assurance and Security (IAS), 2010 Sixth International Conference on, pp. 265–270. IEEE, 2010, Atlanta, GA, USA

  13. Jones AK, Sielken RS (2000) Computer system intrusion detection: A survey. (Online) http:// www.cs.virginia.edu/_jones/IDS-research/Documents/jones-sielken-survey-v11.pdf

  14. Keiko H, David GR, Eduardo FM, Eduardo BF (2013) An analysis of security issues for cloud computing. J Internet Serv Appl 4(5):1–13

    Google Scholar 

  15. Modi C, Patel D, Borisaniya B, Patel H, Patel A, Rajarajan M (2013) A survey of intrusion detection techniques in Cloud. J Netw Comput Appl 36(1):42–57

    Article  Google Scholar 

  16. Gens F (2008) IT Cloud Services User Survey, pt.2: Top Benefits & Challenges. (Online) http://blogs.idc.com/ie/?p=210

  17. Nirmala AP, Sridaran R (2012) Cloud computing issues at design and implementation levels-A survey. Int J Adv Netw Appl 3(6):1444–1449

    Google Scholar 

  18. Vikrant GD, Atul GB, Nikhil AA (2013) Intrusion detection system for cloud computing. Int J Eng Res Technol (IJERT) 2(4):2149–2153

    Google Scholar 

  19. Farzad S (2012) Secure virtualization for cloud environment using hypervisor-based technology. Int J Mach Learn Comput 2(1):39–45

    Google Scholar 

  20. Vinothina V, Sridaran R, Padmavathi G (2012) A survey on resource allocation strategies in cloud computing. Int J Adv Comput Sci Appl 3(6):97–104

    Google Scholar 

  21. Witcha C, Abdul HA, Mohd NMS, Siriporn C, Surat S (2007) A rough fuzzy hybrid algorithm for computer intrusion detection. Int Arab J Inform Technol 4(3):247–254

    Google Scholar 

  22. Gang W, Jinxing H, Jian M, Lihua H (2010) A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert Syst Appl 37(9):6225–6232

    Article  Google Scholar 

  23. Anderson J (1995) An introduction to neural networks. MIT Press, Cambridge

    MATH  Google Scholar 

  24. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd ed. Prentice-Hall Inc, Englewood Cliffs, NJ

  25. Mahbod T, Ebrahim B, Wei L, Ali A, Ghorbani A (2009) Detailed analysis of the KDD CUP 99 data set. In: Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defence Applications 2009, pp.1–7, Ottawa, Canada

  26. Shanmugavadivu R, Nagarajan N (2011) Network intrusion detection system using fuzzy logic. Ind J Comput Sci Eng 2(1):101–111

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, India

    N. Pandeeswari & Ganesh Kumar

Authors
  1. N. Pandeeswari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ganesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. Pandeeswari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandeeswari, N., Kumar, G. Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN. Mobile Netw Appl 21, 494–505 (2016). https://doi.org/10.1007/s11036-015-0644-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-015-0644-x

Keywords

Search

Navigation